Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.
Professor Emily S. Cross is a cognitive and social neuroscientist holding dual appointments at the University of Glasgow's School of Psychology & Neuroscience (UK) and Western Sydney University's MARCS Institute (Australia). She directs the Social Brain in Action Laboratory (SoBA), specializing in how embodied experiences shape social perception. Cross earned a BA in psychology and dance from Pomona College, MSc from University of Otago as a Fulbright Fellow, and PhD in cognitive neuroscience from Dartmouth College, followed by postdoctoral training at University of Nottingham and Max Planck Institute. Her research explores experience-dependent plasticity through dance, robotics, and neuroimaging techniques (fMRI, TMS), focusing on four key areas: neural signatures of embodied expertise, neuroaesthetics, visual learning across lifespan, and social engagement with robots. This interdisciplinary work bridges neuroscience, performing arts, and robotics. Cross's research shows strong focus on human-robot interaction dynamics, cultural perceptions of robotics, and the neural correlates of aesthetic experiences. Recent work investigates how social experience shapes human-robot interaction, cross-cultural differences in robot acceptance, and computational approaches to movement analysis. Publications consistently demonstrate methodological innovation through VR, economic games, and large-scale motion capture libraries. Honors & Awards: Jacob Brownowski Award (British Science Association, 2017) Philip Leverhulme Prize in Psychology (2018) World's 50 Most Renowned Women in Robotics (2020) Elected member: Young Academy of Europe Elected member: Royal Society of Edinburgh's Young Academy of Scotland Her ERC Starting Grant funds groundbreaking work on human-robot interaction. Cross leads the Social Brain in Action Lab with focus on mentoring next-generation scientists and research ethics training. The lab employs neuroimaging, training paradigms, and diverse methodologies to study action observation across dance, music, and robotics domains.
Tianyi Zhang is a Tenure-Track Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He leads the Human-Centered Software Systems Lab, focusing on AI-driven systems that synergize human expertise with machine intelligence to enhance programming productivity and software reliability. Prior to Purdue, he was a Postdoctoral Fellow at Harvard University under Dr. Elena Glassman and earned his Ph.D. from UCLA (2019) and B.Sc. from Huazhong University of Science and Technology (2013). Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) Bachelor's in Computer Science, Huazhong University of Science and Technology (2013) Research Interests: His work spans Software Engineering, Human-Computer Interaction, and AI. Key areas include program synthesis, interactive debugging tools, autonomous driving system testing, and mitigating biases in AI models. He develops systems like Interpretable Program Synthesis and SQLucid to bridge human and machine intelligence. Recent Trends in Publications: Recent work emphasizes human-in-the-loop AI, including mixed-initiative systems for data wrangling (Dango), interactive program repair, and bias analysis in text representations (STILE). He also explores challenges in autonomous driving testing and LLM-based code generation errors. Awards & Grants: NSF Career Award (2024) Amazon Research Award Showalter Trust Research Award for pre-diabetes research $1.5M NSF grant for software supply chain security Best Paper Honorable Mentions at CHI and VAHC Advising & Teams: Supervises 12+ PhD/Master's students and 30+ research interns. Notable advisees include Bonan Kou (API misuse studies) and Yuan Tian (text-to-SQL systems). Collaborates with Harvard Medical School on healthcare data analysis. Labs & Initiatives: Directs Purdue's Human-Centered Software Systems Lab. Co-founded the Societal Impact Fellows program. Active in open-source projects like Examplore for API usage visualization and JShrink for Java debloating.
Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto, specializing in large-scale data management, data systems, and applied machine learning. His research integrates machine learning techniques into scalable data platforms to enhance the analysis of massive datasets. He holds a PhD from the University of Toronto, an MSc from the University of Maryland, and a Bachelor's degree from the University of Patras. Education: PhD, University of Toronto MSc, University of Maryland at College Park Bachelor's degree, University of Patras, Greece Research Interests: Relational Deep Dive (ReDD): Natural language query execution over unstructured documents Streaming Video Queries (SVQ): Interactive query processing for video streams Reliable Text-to-SQL: Generating accurate SQL queries with human-in-the-loop assistance Machine Learning Integration in Data Systems Publications: Focus on video analytics, query processing, and reliable natural language interfaces. Notable works include optimizing video queries, declarative frameworks for temporal constraints, and abstention-based SQL generation. Awards: Inventor of the Year (1st Prize), University of Toronto (2011) Best Paper Awards at international conferences Entrepreneurship & Advising: Co-founder of Sysomos (Meltwater Group), Aislelabs (Constellation Software), and Workorb Advisor to mapintent and ktau Labs & Teams: Leads research groups developing systems like ReDD and SVQ, emphasizing collaboration between academia and industry.
Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.
Jonathan P. Wong is a Senior Policy Researcher at the RAND Corporation and Professor of Policy Analysis at the RAND School of Public Policy. His work bridges academic research and defense policy, focusing on military strategy, acquisition, force planning, and the integration of emerging technologies into military operations. Education: Ph.D. and M.Phil. in Policy Analysis (Pardee RAND Graduate School), M.A. in Security Studies (Georgetown University), B.A. in Political Science (UC San Diego) Professional Experience: U.S. Marine Corps infantry officer (2001–2011), Consultant at Boston Consulting Group, current RAND researcher and professor Wong’s research interests center on defense innovation, military logistics, artificial intelligence in warfare, space capabilities, and the human dimension of military operations. He has led studies on human-machine teaming, non-lethal weapons, defense acquisition reform, and U.S. military posture in the Indo-Pacific. His work emphasizes practical, policy-relevant insights grounded in operational experience. The trends in his recent publications reveal a deep engagement with modern defense challenges: integrating commercial technologies into military systems, improving acquisition agility, enhancing battlefield awareness, and ensuring that defense innovation serves warfighter needs. His research spans strategic, operational, and tactical levels, often focusing on the U.S. Army and Marine Corps. Wong has not received public recognition in the form of listed awards in the provided texts, but his leadership in congressionally mandated studies and high-impact policy research suggests significant influence in defense circles. Advising & Grants: Co-led major projects such as the Long Range Precision Fires study and research on integrating women into Marine Corps infantry. His work is frequently funded by the Department of Defense and other federal entities, though specific grant details are not listed. Wong is affiliated with RAND’s defense and national security research teams, contributing to strategic analysis and policy development. He is not associated with a formal lab but works within RAND’s collaborative research environment, often partnering with other veterans and defense experts to produce actionable insights for military leaders and policymakers.
Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.
Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University , focusing on Data Management , System Efficiency (e.g., Scalable Data Platforms), and Human Efficiency (e.g., LLM-Based Query Interfaces). His work bridges Database Systems , Machine Learning , and Human-Computer Interaction . Co-founder of four startups: Paradise (1997), Locomatix (2007), Quickstep (2015), and DataChat (2017). Member of SIGMOD 2025 (AE) , CIDR 2024 (Co-Chair) , and other program committees. Research Interests include efficient data analysis algorithms , LLM-based data interaction , and systems security . His group develops platforms combining scalability and user productivity . Scientific Awards include Best Paper Awards at SIGMOD and VLDB, and Fellowships from AAAS, ACM, and IEEE. He also received Teaching Awards at CMU. Professional Activities feature co-founding startups , serving on program committees , and teaching courses like Database Systems and Advanced Database Systems at CMU.
Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.
Teemu Roos is a Professor at the Department of Computer Science , University of Helsinki , and a Principal Investigator for the Complex Systems Computation Group under the Helsinki Institute for Information Technology. He serves as a Supervisor for the Doctoral Programme in Computer Science and leads multiple research initiatives, including Distributed AI in Supercomputing , AI & Kids , and Generation AI . Dr. Roos also holds a Docent title in Computer Science. His research spans Artificial Intelligence , Machine Learning , and Data Science , with a focus on AI education , graph neural networks , Bayesian modeling , and health informatics . He has pioneered tools like Elements of AI , a free online course now translated into 22 EU languages, and explores the ethical implications of AI-generated content in authorship and inventorship. The 15 most recent publications highlight applications in environmental forecasting (e.g., Mediterranean Sea via graph-based deep learning), healthcare (e.g., skin cancer detection with transfer learning), and social media analysis (e.g., explainable AI platforms for K-12 education). Methodologically, his work advances clustering algorithms , dimensionality reduction , and approximate nearest neighbor search . Scientific Awards: Cor Baayen Award (2009) Nokia Foundation Recognition Award (2019) Best Paper Honorable Mention Award (2013) ICT Influencer of the Year 2019 (Vuoden TiVi-vaikuttaja 2019) World Summit AI's Top-50 Innovators in 2020 Dr. Roos has supervised 2 doctoral students and contributed to 163 academic activities , including invited talks at MIT, University of Cambridge, and the Finnish Institute in Rome. He has secured funding from the Academy of Finland and the Strategic Research Council, focusing on projects like Fast AI-assisted Space Environment Prediction and Urban Exerciser .
Dr. İsmail ÖZTEL serves as an Assistant Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences. He has been a faculty member since 2019, following his tenure as a Research Assistant from 2012-2019 at the same institution. His educational background includes: Doctorate in Computer and Information Engineering (2014-2018) from Sakarya University with thesis on "Facial expression detection on partial and full face images using machine learning methods" Master's Degree in Computer and Information Engineering (2012-2014) from Sakarya University with thesis on "Driver simulator for educational purposes" Bachelor's Degree in Computer Engineering (2007-2011) from Sakarya University Dr. ÖZTEL's research focuses on artificial intelligence, deep learning, and computer vision with significant applications in healthcare, mobile technology, and public safety. His work has evolved from foundational facial expression recognition to sophisticated medical applications including skin disease classification using smartphones, monkeypox detection from skin lesions, and pandemic response systems for face mask detection. His research demonstrates strong interdisciplinary connections between computer science and healthcare. An analysis of his publication trends reveals a clear progression toward increasingly complex deep learning architectures applied to real-world problems, with recent work emphasizing medical applications using mobile technology and public health safety systems. His 2023-2025 publications show particular focus on skin disease classification, intelligent vehicle systems, and hybrid feature extraction methods for pandemic response. Dr. ÖZTEL has served as a reviewer for numerous prestigious journals including Expert Systems With Applications (multiple years), World Wide Web, Multimedia Tools and Applications, and Journal of King Saud University - Computer and Information Sciences, demonstrating his recognition in the academic community across multiple domains. His research projects include work on facial expression detection in open scientific databases (2020), performance evaluation of transfer learning approaches (2019), and current projects on brain tumor classification systems and earthquake safety education for individuals with developmental disabilities. His international research experience includes collaboration with Filiz Bunyak in 2017, indicating global engagement in his field.
Dr. John Warren Huntley is an Associate Professor in the Department of Geological Sciences at the University of Missouri. His research examines the fossil record of biotic interactions including parasitism, predation, and competition, with additional focus on conservation paleobiology and morphological disparity evolution. Dr. Huntley investigates host-parasite dynamics in marine bivalves, human impacts on mollusk populations, and Phanerozoic predation patterns. His work integrates stratigraphic paleobiology with ecological and evolutionary analyses to reconstruct historical ecosystem dynamics. His recent publications demonstrate expertise in paleoparasitology, taphonomy, and conservation paleobiology, applying novel analytical methods to questions about parasite-host coevolution, preservation biases, and extinction events. This research provides critical insights into long-term ecological patterns and biological responses to environmental change. Dr. Huntley has received significant recognition including an NSF CAREER Award and Humboldt Research Fellowship, and he co-edited the comprehensive volumes 'The Evolution and Fossil Record of Parasitism'.
Aaron J. Elmore is an Associate Professor in the Department of Computer Science and the College of the University of Chicago. His research focuses on cloud computing, databases, and distributed systems, with an emphasis on resource-efficient database execution and collaborative analytics. PhD in Computer Science from University of California, Santa Barbara MS in Computer Science from University of Chicago Research interests include: Elastic databases and multitenancy (Database-as-a-Service) Resource-efficient systems (CrocodileDB, DenseStore, EdgeTSD) Database versioning (Datahub, Decible, OrpheusDB) Data discovery (DataSwamp, Relic) Recent publications highlight advancements in cloud-native query execution, dynamic compression frameworks, and time-series anomaly detection. His work often bridges systems design with practical data science applications. Scientific awards include: NSF CAREER Award (2021) Multiple Google and Intel research grants ACM SIGMOD Best Demo Honorable Mention Aaron has advised multiple PhD students including Jun Hyuk Chang and Riki Otaki, with former advisees now at institutions like MIT, Harvard, and UC Berkeley. He leads the ChiDATA research group and collaborates with Systems Group and CERES Center.
David Mimno is an Associate Professor and Chair of the Department of Information Science at Cornell University. He holds a PhD from the University of Massachusetts Amherst and previously worked at the Perseus Project and Princeton University. His research focuses on computational social science, natural language processing, and historical text analysis. Mimno is known for developing the MALLET toolkit, a widely used Java-based platform for machine learning in text processing. He teaches courses such as INFO 4940: How LLMs Work and INFO 6150/CS 6788: Advanced Topic Modeling. His work has been supported by the Sloan Foundation, NEH, and NSF. Mimno advises PhD students in Information Science and Computer Science, emphasizing interdisciplinary research at the intersection of computing and humanities/social sciences. He also contributes to initiatives like AI for Humanists, making large language models accessible for text-as-data research. Bachelor’s degree: Not explicitly stated in text PhD: University of Massachusetts Amherst Research Interests: Mimno explores large language models, topic modeling, cross-lingual semantics, and ethical AI applications in humanities and legal domains. His recent work addresses data curation practices for language models, LLM memorization of poetry, and generative AI’s societal impacts. He co-authored reports on generative AI in academic research and education. Grants & Collaborations: His projects include the Text as Data (TADA) conference and collaborations on generative AI law workshops. Mimno’s MALLET toolkit supports document classification, clustering, and topic modeling, with applications in cultural analytics and computational historiography.
Jon Atle Gulla is a Professor at NTNU and Director of the Norwegian Research Centre for AI Innovation (NorwAI). He holds academic leadership roles, including former Head of the Department of Computer Science and Informatics at NTNU. His expertise spans Semantics, Language Technology, Recommender Systems, and AI-driven innovation. He has nearly 150 international publications and advised over 100 students across MSc, PhD, and postdoctoral levels. Education: MSc in Computer Science (1988), PhD in Computer Science (1993) from Norwegian Institute of Technology (NTH) MSc in Linguistics (1995), University of Trondheim MSc in Management (Sloan fellowship, 2003), London Business School Research interests focus on Semantics and Language Technology applied to Recommender Systems, Information Retrieval, and Text Analysis. He explores AI-based innovations in digitalization and entrepreneurship, advising industry on AI adoption and commercialization. Notable projects include Big Data collaborations with DNB, RecTech for news recommendation, and Trondheim Analytica analyzing political texts/social media. Publications emphasize AI applications in news recommendation, political text analysis, and Scandinavian language models. His work addresses ethical AI, copyright implications, and cross-lingual NLP challenges. Awards: Member of the Royal Norwegian Society of Arts and Sciences. Advising/grants: Supervised 30 PhD students and 70 MSc students. Involved in startups like Fast Search & Transfer (acquired by Microsoft) and Mito.ai/Strise.ai. Active in reviewing for journals like Data & Knowledge Engineering and conferences like ACL. Labs/teams: Leads NorwAI, co-founder of INRA and NOBIDS workshops. Collaborates with industry and academia on AI-driven solutions.
